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The Impact of Stratospheric Circulation Extremes on Minimum Arctic Sea Ice Extent
KAREN L. SMITHa
Lamont-Doherty Earth Observatory, Palisades, New York
LORENZO M. POLVANI
Department of Applied Physics and Applied Mathematics, and Department of Earth and Environmental Sciences,
Columbia University, New York, New York
L. BRUNO TREMBLAY
Department of Atmospheric and Oceanic Sciences, McGill University, Montreal, Quebec, Canada
(Manuscript received 24 July 2017, in final form 17 April 2018)
ABSTRACT
Given the rapidly changing Arctic climate, there is an urgent need for improved seasonal predictions of Arctic
sea ice. Yet, Arctic sea ice prediction is inherently complex. Among other factors, wintertime atmospheric cir-
culation has been shown to be predictive of summertime Arctic sea ice extent. Specifically, many studies have
shown that the interannual variability of summertimeArctic sea ice extent (SIE) is anticorrelatedwith the leading
mode of extratropical atmospheric variability, the Arctic Oscillation (AO), in the preceding winter. Given this
relationship, the potential predictive role of stratospheric circulation extremes and stratosphere–troposphere
coupling in linking the AO and Arctic SIE variability is examined. It is shown that extremes in the stratospheric
circulation during the winter season, namely, stratospheric sudden warming (SSW) and strong polar vortex (SPV)
events, are associatedwith significant anomalies in sea ice concentration in theBarents Sea in spring and along the
Eurasian coastline in summer in both observations and a fully coupled, stratosphere-resolving general circulation
model. Consistent with previous work on theAO, it is shown that SSWs, which are followed by the negative phase
of the AO at the surface, result in sea ice growth, whereas SPVs, which are followed by the positive phase of the
AO at the surface, result in sea ice loss, although the mechanisms in the Barents Sea and along the Eurasian
coastline are different. The analysis suggests that the presence or absence of stratospheric circulation extremes in
winter may play a nontrivial role in determining total September Arctic SIE when combined with other factors.
1. Introduction
Since 2007, a year of, what was at the time, un-
precedented Arctic sea ice loss, there has been a sub-
stantial and organized effort to improve seasonal Arctic
sea ice prediction in order to better inform industries
and communities that depend on reliable sea ice in-
formation (Stroeve et al. 2015; Jung et al. 2016). To that
end, recent studies have focused on identifying key
sources of Arctic sea ice predictability to improve both
statistical and dynamical sea ice forecasts (Petty et al.
2017; Bushuk et al. 2017; Chen et al. 2017; Schröder et al.2014; Stroeve et al. 2014b; Tietsche et al. 2013).
Among other factors, tropospheric circulation anom-
alies have been shown to be predictive of interannual
variability in Arctic sea ice (Rigor et al. 2002; Ogi et al.
2010). Specifically, studies have demonstrated an anti-
correlation between the leading modes of extratropical
atmospheric variability, theArctic Oscillation (AO) and
the North Atlantic Oscillation (NAO), in winter and
spring and Arctic sea ice extent in summer (Rigor et al.
2002; Rigor and Wallace 2004; Strong et al. 2009; Ogi
et al. 2010; Wu and Zhang 2010; Stroeve et al. 2011;
Wettstein and Deser 2014; Williams et al. 2016). In the
presence of rapid Arctic sea ice decline, the relationship
Supplemental information related to this paper is available at the
Journals Online website: https://doi.org/10.1175/JCLI-D-17-0495.s1.
a Current affiliation: Department of Physical and Environmental
Sciences, University of Toronto Scarborough, Toronto, Ontario,
Canada.
Corresponding author: Karen L. Smith, [email protected]
VOLUME 31 J OURNAL OF CL IMATE 15 SEPTEMBER 2018
DOI: 10.1175/JCLI-D-17-0495.1
� 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS CopyrightPolicy (www.ametsoc.org/PUBSReuseLicenses).
7169
between the AO and sea ice extent is complex (Holland
and Stroeve 2011; Ogi et al. 2010), yet recent work
demonstrates the increasing predictive skill of winter-
time atmospheric circulation on the September Arctic
sea ice minimum (Kimura et al. 2013; Williams et al.
2016; Itkin and Krumpen 2017). Williams et al. (2016)
emphasizes that the dramatic loss of thick, multiyear ice
has led to enhanced atmosphere–sea ice coupling. When
this is taken into account, one finds a significant anti-
correlation between the late winter AO and September
Arctic sea ice extent (SIE; Williams et al. 2016). The
anticorrelation is due, in part, to increased sea ice di-
vergence along the East Siberian Sea and Laptev Sea
coastlines when the AO is in its positive phase. This
increased divergence along the Eurasian coastline pro-
motes growth of thin, first-year ice that melts more
readily throughout the summer months, leading to an
earlier retreat that is amplified by the ice–albedo feed-
back and resulting in anomalously low SeptemberArctic
SIE (Stroeve et al. 2012; Itkin and Krumpen 2017).
Given this relationship between Arctic SIE and the
AO/NAO and the need for better seasonal predictions
of Arctic SIE (Stroeve et al. 2015; Jung et al. 2016), our
aim is to extend previous work to examine whether
wintertime stratospheric circulation plays a role in
Arctic sea ice variability. Interannual anomalies in the
Arctic stratospheric circulation migrate downward to
the troposphere, projecting strongly onto the AO and
NAO. Notably, it is well established that stratospheric
sudden warmings (SSWs), abrupt reversals of the win-
tertime stratospheric polar vortex, precede negative
phases of the AO/NAO. Thus, SSWs could potentially
lead positive anomalies in Arctic SIE and, vice versa,
the absence of SSWs may be associated with negative
anomalies in SIE. These stratosphere–troposphere-
coupled AO/NAO anomalies tend to persist for ;40–
60 days (Baldwin and Dunkerton 2001; Charlton-Perez
et al. 2013), leading to enhanced predictability in the
troposphere in winter and early spring following large
stratospheric circulationanomalies (BaldwinandDunkerton
2001; Sigmond et al. 2013; Scaife et al. 2016). This
persistence may result in a relatively strong influence of
the stratospheric circulation on Arctic sea ice via the
AO/NAO.
On longer time scales, the observed, accelerated loss
of Arctic sea ice concentration in the 1990s has been
associated with the positive trend in theAO (Rigor et al.
2002). This positive trend in the AO has itself been
linked to the lack of SSWs and weakened stratosphere–
troposphere coupling during that decade (Scaife et al.
2005; Douville 2009), suggesting a potential connection
between the stratospheric circulation and Arctic sea
ice. As seasonal forecasts of Arctic sea ice become
increasingly important in a warming climate, wintertime
stratospheric information may help increase the pre-
dictive skill and lead times for these forecasts (Sigmond
et al. 2013; Scaife et al. 2016).
In this paper, we show that extremes in the strato-
spheric circulation during thewinter season are followed
by significant anomalies in SIE along the Eurasian
coastline in summer in both observations and fully
coupled, stratosphere-resolving general circulation
model integrations (GCM). Our goal here is to present
evidence for a potential stratospheric source of Arctic
sea ice predictability and further work is required to
quantify the detailed contributions to the dynamical and
thermodynamical processes involved.
In section 2, we describe the GCM and observational
data used and define stratospheric circulation extremes,
namely, SSW and strong polar vortex (SPV) events.
Because the number of SSW and SPV events in the
observational record is small we start, in section 3, by
presenting an analysis of a 400-yr-long GCM control
integration to establish robust signals and to examine
the mechanisms. We then turn to the observations
(section 4) and show that they are in good agreement
with the GCM results, albeit less statistically robust
owing to the shorter record.
2. Methods
a. Model and control integration
We analyze a long, time-slice integration of theWhole
Atmosphere Community Climate Model (WACCM),
the stratosphere-resolving, coupled-chemistry version
of the NCAR Community Earth SystemModel, version
1 [CESM1(WACCM)]. The atmospheric component of
WACCM has 66 vertical levels with a model top at
140 km, a horizontal resolution of 1.98 3 2.58 specializedparameterizations for gravity waves, and other upper-
atmospheric processes and fully interactive middle at-
mosphere chemistry. In the integration examined here,
WACCM is coupled to identical interactive land, ocean,
and sea ice models as in the simulations conducted as
part of phase 5 of the Coupled Model Intercomparison
Project (CMIP5), described in Marsh et al. (2013).
It is important to note that WACCM has an annual
mean Arctic SIE that is well within the spread of the
CMIP5 models for the 1979–2005 time period [13.44 vs
the multimodel mean of 12.81 million km2, see Shu et al.
(2015)]. However, the climatological WACCM sea ice
thickness is biased high relative to observationally based
data products (Stroeve et al. 2014a), such as the Pan-
Arctic Ice Ocean Assimilation System (PIOMAS;
Zhang and Rothrock 2003). In particular, WACCM
7170 JOURNAL OF CL IMATE VOLUME 31
has a relatively high sea ice thickness bias along the
Eurasian coastline in winter and spring. (For reference,
in the supplementary material Figs. S1, S3, and S5 show
theWACCMclimatological mean sea ice concentration,
thickness, and velocity and Figs. S2 and S4 show the
climatological standard deviation of sea ice concentra-
tion and thickness.)
The WACCM integration used in this study is a
400-yr-long, time-slice integration, with all forcings, in-
cluding CO2, ozone-depleting substances, aerosols, etc.,
set at constant year-2000 values. Daily model output is
available for all atmospheric fields of interest (zonal
wind, sea level pressure, surface wind stress), as well as
for sea ice concentration. All other sea ice fields, such as
thickness, ice velocities and concentration and volume
tendencies, are available at monthly resolution.
b. Observational data
As discussed in the introduction, our study was ini-
tially motivated by observational analysis, which we
discuss at the end of the paper (section 4). For that
analysis, we use ERA-Interim reanalysis data from 1979
to 2015 (Dee et al. 2011) to identify anomalies in the
stratospheric circulation and we use the daily National
Snow and Ice Data Center (NSIDC) bootstrap sea ice
concentration (SIC) data from 1979 to 2015 (Comiso
2000, updated 2015). We linearly interpolate the every-
other-daily NSIDC SIC data from 31 December 1978 to
31 July 1987 to daily resolution, and combine these with
the daily data from 1 August 1987 to 31 December 2015.
Missing data between 3 December 1987 and 12 January
1988 are replaced with a daily climatology representa-
tive of the early part of the Arctic sea ice record, the
1979–1992 daily average. Within the polar data gap, a
region over the pole where satellite coverage is in-
complete [the NSIDC SIC data contain a circular data
mask centered on the pole with a radius ranging from
611km in 1979, 311km in 1988, and 94km at present,
Strong andGolden (2016)], we replacemissing data with
100% SIC. The SIC data were then regridded from the
SSM/I polar-stereographic grid to the WACCM 18 3 18latitude–longitude sea ice grid using the NSIDC geo-
coordinate tools. We detrend the SIC data using a
quadratic fit; however, our composite analysis is quali-
tatively insensitive to whether we linearly or quadrati-
cally detrend the SIC data.
c. SSW and SPV identification and composite analysis
We examine the relationship between anomalies in
the stratospheric circulation and Arctic sea ice by per-
forming composite analysis of extremes in polar strato-
spheric zonal mean zonal wind, namely, SSWs and
SPVs. The climatological wintertime stratospheric polar
vortex is characterized by strong westerly winds. SSWs
are abrupt transitions of this westerly wind to easterly
conditions. SSWs are identified when the daily zonal
mean zonal wind at 608N and 10hPa becomes easterly
(the first day that satisfies this criterion is called the
‘‘central date’’). Individual SSW events within the same
winter season must be separated by at least 20 days of
westerlies and we exclude all final warmings (Charlton
and Polvani 2007). Using the above definition, the fre-
quency of SSWs in global reanalysis products is ap-
proximately five events per decade (Butler et al. 2015).
SPVs are identified in a similar way, when the daily
zonal mean zonal wind at 608N and 10hPa exceeds a
threshold of 48ms21 (Scaife et al. 2016). This threshold
is somewhat arbitrary, but yields a similar number of
SPVs as SSWs in both the WACCM simulation and the
reanalysis. As for SSWs, we require that individual SPVs
within the same winter season be separated by at least
20 days of winds that are weaker than the threshold.
We perform two composite analyses. The first is based
on the central date of the SSW or SPV. This method of
composite analysis requires daily model output. The
second is based on seasonal averages for years with
SSWs or SPVs using monthly model output. For both
methods, we have excluded years in which both a SSW
and a SPV occur in the same year. For our 400-yr-long
control simulation, using the first method, we initially
have 193 SSWs (composite mean central date of
26 January, and standard deviation of 38 days) and 156
SPVs (composite mean central date of 16 January, and
standard deviation of 29 days). When we remove over-
lapping years (years in which we have both a SSW and a
SPV) and account for the fact that some years havemore
than one SSW or SPV, we end up with 126 winters with
SSWs and 99 winters with SPVs.
As for the reanalysis, using the first method initially
yields 21 SSWs (composite mean central date of
26 January) and 26 SPVs (composite mean central date
of 23 December) for 1979–2015 (see supplementary
Table S1). If we remove overlapping years, we end up
with 8 winters with SSWs and 14 winters with SPVs.
3. Modeling evidence of a stratospheric impact onArctic sea ice
a. Signatures of stratospheric circulation extremes
Before examining the relationship between extremes
in the stratospheric circulation and Arctic sea ice, it is
important to first examine the characteristics of SSWs
and SPVs in WACCM and demonstrate that they are in
good agreement with ERA-Interim reanalysis, and then
to show that the surface signatures of SSWs and SPVs
15 SEPTEMBER 2018 SM I TH ET AL . 7171
are robust to the different composite methods described
above.
First, Figs. 1a and 1b show the composite mean daily
zonal mean zonal wind anomalies normalized by the
daily standard deviation at 608N (U608N) for SSWs and
SPVs as a function of pressure and lag about the central
date (day zero is the central date of the SSW or SPV
event). We use normalized zonal mean zonal wind
anomalies to more clearly bring out the anomalies in the
troposphere. We find that the SSW and SPV definitions
clearly reveal extremes in the stratospheric circulation
that migrate down to the troposphere and that compare
well with reanalysis (Figs. 1c and 1d).
Second, we examine the surface climate anomalies
following extremes in the stratospheric circulation.
Figures 2a and 2b show the composite mean sea level
pressure (SLP) and surface wind stress (t) anomalies in
WACCM averaged over days 0–40 following the central
dates of the SSWs and SPVs, respectively. We find that
the pattern of SLP and t anomalies closely resemble
the negative (Fig. 2a) and positive (Fig. 2b) phases
of the AO, and, in the Atlantic region, the NAO. The
surface signatures of these extremes are not entirely
antisymmetric, with stronger stratosphere–troposphere
coupling following SSWs (Baldwin andDunkerton 2001;
Scaife et al. 2016).
Now we compare the above SSW and SPV com-
posites, based on the central dates of the events, to the
seasonal composites of winters with SSWs or SPVs
[January–February–March (JFM); Figs. 2c and 2d].
Although the tropospheric circulation anomalies
based on these seasonal composites are not identical
to those based on the central date, we see a qualitative
similarity between the two composite methods, sug-
gesting that the presence of a SSW or SPV in a par-
ticular winter is the dominant influence on the
seasonal mean tropospheric circulation pattern. By
extension, using this seasonal composite method to
examine sea ice concentration anomalies, we can
show the influence of stratospheric circulation ex-
tremes in winter on Arctic sea ice. The correspon-
dence between the composite methods also allows us
to make use of the monthly model output to examine
the relationship between stratospheric circulation
extremes and Arctic sea ice in greater detail. Recall,
that for sea ice variables, we only have daily model
FIG. 1. Composite and zonal mean zonal wind at 608N normalized by its standard deviation (U608N; color
shading) as a function of lag (days) and pressure (hPa) for (a) SSWs and (b) SPVs in WACCM, and (c) SSWs and
(d) SPVs in ERA-Interim. Day zero corresponds to the central date of the SSW or SPV event. Therefore, positive
lags indicate that the SSWor SPV event is leading. Grid points that are statistically significant at the 95% level using
a Student’s t test are hatched. Here, we are showing composite means for ERA-Interim using all events, 21 SSWs
and 26 SPVs, but the results are qualitatively similar for the reduced composite sizes where years containing both
SSWs and SPVs have been removed.
7172 JOURNAL OF CL IMATE VOLUME 31
output for SIC—all other sea ice variables were only
output at monthly resolution.
b. Stratospheric influence on Arctic sea ice
Having established the features of stratospheric cir-
culation extremes in WACCM, and having shown that
these features are robust to the compositing methods
used, we now turn to anomalies in Arctic sea ice fol-
lowing SSWs and SPVs. Below, we first establish the
presence of SIE and SIC anomalies following SSWs and
SPVs and then in section 3c, we describe the physical
processes in more detail.
First, we examine whether SSWs or SPVs are followed
by anomalies in total Arctic SIE. In particular, we are
interested in whether SSWs or SPVs are associated with
anomalies in the Arctic SIE minimum in the following
September. Figures 3a and 3b show composite mean
time series of total SIE as a function of lag for SSWs and
SPVs, respectively. We find significant anomalies in to-
tal SIE up to ;180 days following SSWs and SPVs. As
expected for the sign of the AO/NAO, we find that
SSWs are associated with positive SIE anomalies, while
SPVs are associated with negative SIE anomalies. The
SSW and SPV composite mean central dates are 26 and
16 January, respectively, resulting in significant total SIE
anomalies stretching into July, but not September.
However, the total SIE is not the whole story.
Examination of the SIE anomalies in different regions
within the Arctic show that the anomalies in total SIE
arise primarily from anomalies in the Barents Sea (BA)
and the Bering Strait and Sea of Okhotsk (B/O)
(Figs. 3c,d,g,h). While anomalies in these seas do not
FIG. 2. Composite mean SLP (color shading; units are hPa) and surface wind stress (vectors; units are Pa)
anomalies in WACCM for (a) days 0–40 following the SSW central date, (b) days 0–40 following the SPV central
date, (c) JFM of years with SSWs, and (d) JFM for years with SPVs. Composites consist of 126 SSWs and 99 SPVs.
Grid points of SLP within the red contour are statistically significant at the 95% level using a Student’s t test. Only
statistically significant wind stress vectors are displayed.
15 SEPTEMBER 2018 SM I TH ET AL . 7173
persist into September, we do find significant SIE
anomalies in the Laptev, East Siberian, and Chukchi
Seas (L/ES/C) following SSWs and SPVs that emerge in
summer and persist into autumn (Figs. 3e,f). The reason
for focusing only on the above regions in Fig. 3 will
become clear below, when we discuss Fig. 4 and the
spatiotemporal nature of the SIC anomalies.
The timing of the SIE anomalies shown in Fig. 3
highlights the nature of these anomalies in the different
Arctic seas. For example, sea ice in the BA region is
relatively thin and, therefore, responds quickly to wind
anomalies associated with the AO/NAO via anomalous
ice advection and ocean heat flux (Bitz et al. 2005;
Sorteberg and Kvingedal 2006; Koenigk et al. 2009;
FIG. 3. Composite mean SIE anomaly time series as a function of lag (days) for (left) SSWs and (right) SPVs in
WACCM. (a),(b) Total SIE anomalies; (c),(d) Barents Sea (708–808N, 208–608E); (e),(f) Laptev, East Siberian, andChukchi Seas (658–808N, 928–2008E); and (g),(h) Bering Strait (508–658N, 1628–2108E) and Sea of Okhotsk (458–658N, 1308–1628E). Composites consist of 126 SSWs and 99 SPVs. Days that are statistically significant at the 95%
level using a Student’s t test are indicated by the solid red line. Gray shading shows61 standard deviation across the
SSW and SPV events. The black vertical line at day zero indicates the central date of the SSW and SPV events. The
regions were chosen based on the temporal and spatial coherence of SIC anomalies (see Fig. 4).
7174 JOURNAL OF CL IMATE VOLUME 31
Strong and Magnusdottir 2010; Arthun et al. 2012),
whereas sea ice in the L/ES/C region is thicker and,
therefore, has memory of winter conditions that extends
well into summer (Williams et al. 2016).Wewill quantify
these processes further in section 3c below.
We are aware that there has been considerable re-
search interest in the opposite influence, that is, the
impact of Arctic sea ice anomalies on the stratospheric
circulation (Scinocca et al. 2009; Cai et al. 2012; Sun
et al. 2014; Peings andMagnusdottir 2014; Feldstein and
Lee 2014; Kim et al. 2014; Sun et al. 2015; Wu and Smith
2016; Yang et al. 2016; Zhang et al. 2018); however, this
is not the focus of our study. Here, we are interested in
identifying and examining how stratospheric circulation
extremes drive SIE anomalies. There is some evidence
in Figs. 3c–f that SIE anomalies in the BA and the B/O
regions may develop before the SSW and SPV central
dates (day zero), and, thus, may, in fact, be sources of
anomalous upward wave activity flux driving these
events (Yang et al. 2016; Zhang et al. 2018; Garfinkel
et al. 2012). In the BA region, the anomalies that co-
incide with the central date do not persist and, in fact,
change sign, suggesting we may potentially treat the
anomalies following the central dates independently.
The anomalies preceding the central dates in the B/O
region persist for months after the central dates, also
suggesting that regardless of whether the anomalies
existed before the central date, SSWs or SPVs may
contribute to the persistence of the B/O anomalies.
We also note here that El Niño and La Niña are as-
sociated with significant SIC anomalies in the B/O re-
gion inWACCM (but, not in the BA or L/ES/C regions)
and that the concurrence of El Niño and LaNiña winterswith our SSW and SPV winters is not evenly distributed
(Polvani et al. 2017). Of our 126 SSW winters, 61 are El
Niño years and 47 are LaNiña years, while of the 99 SPVwinters, 15 are El Niño winters and 11 are La Niñawinters. Because it is difficult to disentangle the lead–lag
relationships without performing sea ice perturbation
experiments (beyond the scope of this paper) and also
difficult to quantify the relative importance of ENSO
without significantly reducing our composite sizes, we
simply focus on the SIE anomalies in the BA region and
along the Eurasian coastline following all SSWs and
SPVs, irrespective of the ENSO phase.
With these caveats in mind, we now turn to the sea-
sonal compositemethod and examine the spatial pattern
of SIC anomalies in more detail (see supplementary
FIG. 4. Composite mean seasonal sea ice concentration anomalies for (a)–(c) years with SSWs and (d)–(f) years with SPVs. Seasonal
means are shown for (a),(d) JFM; (b),(e) AMJ; and (c),(f) JAS. Composites consist of 126 SSWs and 99 SPVs. Grid points that are
statistically significant at the 95% level using a Student’s t test are hatched.
15 SEPTEMBER 2018 SM I TH ET AL . 7175
Fig. S6 for similar plots using the central data composite
method). The regional SIE anomalies shown in Fig. 3
are clearly visible in Fig. 4. In Figs. 4a and 4d, which
show the JFM composite mean SIC anomalies for SSW
and SPV years, respectively, we can see the significant
anomalies in the B/O region (as in Figs. 3g and 3h).
In April–May–June (AMJ), we see the full develop-
ment of SIC anomalies in the BA region (Figs. 4b and
4e). These anomalies are quite localized, but relatively
large, up to ;12%. Relative to the interannual vari-
ability of the entire 400-yr-long WACCM integration,
the composite mean anomalies, area averaged over the
BA region, represent approximately one-quarter of a
standard deviation for both SSWs and SPVs.
Finally, in July–August–September (JAS), we see the
emergence of SIC anomalies in the Kara, Laptev, East
Siberian, and Chukchi Seas, regions where sea ice con-
centration in summer is strongly correlated with the
AO/NAO in winter (Figs. 4c and 4f; Williams et al.
2016). We find positive anomalies following SSWs
(Fig. 4c) and negative anomalies following SPVs (Fig. 4f),
consistent with the relationship between stratospheric
circulation extremes and the AO/NAO. The composite
mean anomalies, area averaged over the L/ES/C region,
are approximately one-quarter of a standard deviation
for SSWs and one-half for SPVs.
c. Processes linking the stratosphere andArctic sea ice
Having clearly shown that stratospheric circulation
extremes are followed by anomalies in Arctic SIC in
WACCM, we now explore the processes giving rise to
these anomalies. We identify, in general terms, which
factors contribute to the temporal evolution of SIC
anomalies in different regions of the Arctic.
We begin by examining anomalies in sea ice thickness.
Large anomalies in sea ice thickness collocated with SIC
anomalies of the same sign indicate SIC loss or gain via
sea ice divergence or convergence within the pack ice,
which is known to result in subsequent early or late
summer ice retreat (Itkin andKrumpen 2017; Chevallier
and Salas-Mélia 2012). In contrast, in regions that are
directly connected to the Atlantic and Pacific Oceans,
where ice is typically thin, and the ocean is not 100%
ice covered, SIC anomalies tend to be associated with
thickness anomalies that are small inmagnitude.Wewill
see below that, contributions from both anomalous ice
advection and thermodynamic processes, such as at-
mospheric and ocean heat transport appear to be im-
portant in these regions in WACCM (Bitz et al. 2005;
Koenigk et al. 2009; Strong and Magnusdottir 2010).
Figures 5a,b and 5d,e show JFM and AMJ composite
mean thickness anomalies for SSW and SPV years,
FIG. 5. As Fig. 4, but for sea ice thickness anomalies.
7176 JOURNAL OF CL IMATE VOLUME 31
respectively. The model shows significant thickness
anomalies in the Beaufort Sea, although there are no
significant SIC anomalies in that region. Positive
anomalies are likely indicative of thicker multiyear ice
advection by the Beaufort Gyre into the Beaufort Sea
associated with the positive phase of the AO. We also
see only small magnitude thickness anomalies in the BA
the B/O regions where the sea ice is climatologically
thin. Fig. S7 is identical to Fig. 5 except that the thickness
anomalies are shown as a percentage of the climato-
logical standard deviation. In this figure, the size of
the thickness anomalies with respect to interannual
variability is more readily seen, with the anomalies in
the BA region being up to 30%–40% of a standard
deviation.
In AMJ, we begin to see small anomalies in thickness
emerge along the Eurasian coastline and in the Chukchi
Sea. By JAS, we see significant thickness anomalies in
the L/ES/C region for the SSW and SPV composites,
respectively (Figs. 5c and 5f). Positive thickness anom-
alies are a proxy for coastal sea ice convergence and
delayed ice retreat, while negative thickness anomalies
are a proxy for divergence and earlier ice retreat. These
thickness anomalies are collocated with the JAS SIC
anomalies shown in Fig. 4, suggesting that the thickness
anomalies contribute to the SIC anomalies by either
delaying (SSWs) or accelerating (SPVs) summer ice
retreat. Area averaged over the L/ES/C region, the
thickness anomalies are approximately 16% of a stan-
dard deviation for SSWs and 32% for SPVs (see Fig. S7).
We now explore the time-dependent development of
the SIC and thickness anomalies in more detail for the
two regions of interest: the BA and the L/ES/C regions.
Specifically, we will examine the dynamical and ther-
modynamical contributions to the SIC and thickness
tendency anomalies (see, e.g., Landrum et al. 2012;
Hunke and Lipscomb 2008; Thorndike et al. 1975). The
sea ice model in WACCM includes a subgrid-scale ice
thickness distribution, discretized into five thickness
categories, that is governed by the following tendency
equation:
›g
›t52
›( fg)
›h2= � (gu)1C , (1)
where u is the horizontal ice velocity, f is the rate of
thermodynamic ice growth, C is a ridging and rafting
redistribution function, and g is the ice thickness distri-
bution. The term g(h)dh is defined as the fractional area
covered by ice in the thickness range h to h 1 dh at a
given time and grid cell. The thermodynamic tendency,
the first term in Eq. (1), includes basal, surface, and
lateral growth and melt processes, while the dynamical
FIG. 6. Time series of running seasonal means of the difference in area-averaged anomalies for (left) the BA region and (right) the L/ES/C
region between SPV and SSW years. (top) Sea ice concentration (green) and concentration tendency anomalies (total 5 black, ther-
modynamical 5 red, dynamical 5 blue). (bottom) Sea ice thickness (green) and volume tendency anomalies (total 5 black, thermody-
namical5 red, dynamical5 blue). Seasons when the difference between SSWs and SPVs is statistically significant at the 95% level using
a Student’s t test are shown with solid lines.
15 SEPTEMBER 2018 SM I TH ET AL . 7177
tendency, the last two terms in Eq. (1), includes advec-
tive and mechanical redistribution processes. The vol-
ume tendency for each ice thickness category is simply
the fractional area multiplied by the thickness.
To condense our findings, in Fig. 6, we now show only
the difference between SPV and SSW years. Figures 6a
and 6b show the area-averaged seasonal time series of
SIC (green curve) and SIC tendency (black, red, and
blue curves) anomalies—the difference between SPV
and SSW years—for the BA and L/ES/C regions, re-
spectively. As inferred from Fig. 4, Figs. 6a and 6b show
that the BA SIC anomalies reach a minimum in spring
and the L/ES/C SIC anomalies reach a minimum in
summer.
The SIC tendency anomalies illustrate the processes
responsible for the development of SIC anomalies. In
the BA region (Fig. 6a), we find that the total SIC ten-
dency anomaly (black curve) consists almost entirely of
the dynamical component in winter and spring (blue
curve), indicating that ice advection is the primary
process. In the L/ES/C region (Fig. 6b), in contrast, we
find that a negative dynamical tendency anomaly is
offset by a positive thermodynamic tendency anomaly in
winter, but that it is the thermodynamical component in
spring and summer that contributes to the summer
minimum.
Figures 7a and 7b show the seasonal evolution of the
SICanomaly—thedifference betweenSPVandSSWyears,
as in Fig. 6—for the five sea ice thickness categories. Ice
advection in the BA region leads to thin ice growth (cate-
gory 1), but this is offset by retreat of thicker ice (categories
2 and 3). Because the thermodynamic SIC tendency is
roughly zero until late spring (Fig. 6a), some of the loss
of the thicker ice is likely due to melt associated with
atmospheric or ocean heat transport, as well as advection.
In the L/ES/C region, ice advection in winter leads to
growth of ice in categories 1 and 2 and retreat of ice in
category 3 (Fig. 7b). This is reflected in the approximate
balance between the dynamic and thermodynamic SIC
tendencies in Fig. 6b. Later in the season, retreat in the
thicker ice categories (categories 4 and 5), associatedwith
thermodynamic processes (Fig. 6b) begin to dominate.
Turning to the volume tendency anomalies in Figs. 6c
and 6d, we also see differences between the two regions
of interest. Although the dynamical volume tendency
anomalies in winter are the largest contributor to the
total in both regions, the thermodynamical volume
tendency anomalies are quite different. In the BA
FIG. 7. Time series of running seasonal means of the difference in area-averaged anomalies for (left) the BA
region and (right) the L/ES/C region between SPV and SSW years. (top) Sea ice concentration anomalies for five
different ice thickness categories. (bottom) Sea ice volume anomalies for the same five ice thickness categories. The
lower bounds of the five ice thickness categories are defined as follows: category 1 5 0.00m, category 2 5 0.64m,
category 35 1.39m, category 45 2.47m, and category 55 4.57m. Seasons when the difference between SSWs and
SPVs is statistically significant at the 95% level using a Student’s t test are shown with solid lines.
7178 JOURNAL OF CL IMATE VOLUME 31
region, the thermodynamical tendency anomaly occurs
essentially at the same time as the dynamical component.
In spring, the thermodynamical tendency anomaly domi-
nates because of the slower response of ocean heat flux to
the NAO, relative to ice advection (Arthun et al. 2012).
However, this thermodynamical component does not ap-
pear to contribute to the mean anomalous SIC minimum
in spring (see Fig. 6a), but rather to a redistribution of ice
thickness in the BA region (Fig. 7a; Thorndike et al. 1975).
In the L/ES/C region, unlike the BA region, where the
volume tendency components tend to covary in time, we
see a delay of several months between the minimum in
the dynamical and thermodynamical tendency compo-
nents. We find that the dynamically driven negative
anomalies in ice volume in winter and spring are not
immediately accompanied by thermodynamically driven
negative anomalies. The sumertime thermodynamically
driven anomalies in ice volume are preceded by gradual
dynamical thinning of the ice. The delay may be related
to the fact that the ice in this region is climatologically
thicker and, thus, it takes several months for the ice to get
thin enough to promote strong feedbacks between ice
thickness and ice melt, which then leads to early onset of
spring/summer retreat.
As above, we show the seasonal evolution of the sea
ice volume anomaly—the difference between SPV and
SSWyears—for five sea ice thickness categories (Figs. 7c
and 7d). In both regions, we see complementary cate-
gorical contributions to the ice volume anomalies as was
shown above for the SIC anomalies (Figs. 7a and 7b).
Further examination of the ice–atmosphere–ocean en-
ergy budget is ongoing in order to attribute these anom-
alies to specific atmospheric and oceanic processes.
4. Observational evidence of a stratospheric impacton Arctic sea ice
Based on model output, we have built a case for the
existence of significant Arctic sea ice anomalies fol-
lowing stratospheric circulation extremes. Using a long
WACCM time-slice integration has allowed us to sep-
arate SSW and SPV events while maintaining large
composite sizes, thus, robust statistics. We now turn to
the observational Arctic sea ice extent record, where the
analysis is complicated by large, nonlinear trends and a
relatively short time series. Despite these issues, we now
show that similar SIC anomalies associated with SSW
and SPV events can be seen in the observations.
FIG. 8. As in Fig. 4, but for ERA-Interim and NSIDC bootstrap sea ice concentration for 1979–2015. Seasonal means are shown for
(a),(d) JFM; (b),(e) AMJ; and (c),(f) JAS.
15 SEPTEMBER 2018 SM I TH ET AL . 7179
As stated above, in the ERA-Interim reanalysis data,
only 8 SSW and 14 SPV nonoverlapping winters are
found since 1979. In Fig. 8 we show the seasonal com-
posite mean SIC for SSW (top row) and SPV (bottom
row) events. Figure 8 can be compared to Fig. 4 (but
note the different color bar range). The observations
show a similar pattern of SIC anomalies in the Sea of
Okhotsk in JFM, the BA region in JFM and AMJ, and
the L/ES/C region in JAS. Given the small number of
events in the composites, statistical significance in Fig. 8
is very noisy and not shown; however, Fig. 9 shows
composite time series of SIE for the BA and L/ES/C
regions following SSW and SPV events with statistical
significance indicated (cf. WACCM in Fig. 3). We see
that the SIE anomalies in these regions are statistically
significant (at the 90% level), despite the small composite
sizes, and that the timing of the anomalies following
SSW and SPV events agrees well with the model. As in
WACCM, we did not find significant anomalies in pan-
Arctic September SIE following stratospheric circulation
extremes in the observational record (not shown).
5. Conclusions
In this study, we have demonstrated a clear rela-
tionship between stratospheric circulation extremes,
namely, stratospheric sudden warmings (SSWs) and
strong polar vortex events (SPVs), and Arctic sea ice
anomalies in the following months, using a long time-
slicemodel integration. Composite analysis of SSWs and
SPVs reveals significant and opposite-signed regional
SIE anomalies: these are found in the Bering Strait and
Sea of Ohkotsk (B/O) in winter, the Barents Sea (BA) in
spring, and the Laptev, East Siberian, and Chukchi Seas
(L/ES/C) in summer.
In the BA region, we show that SIE anomalies are
primarily driven by winter ice advection (Fig. 6),
whereas SIE anomalies in the L/ES/C region are asso-
ciated with sea ice thickness anomalies, initially gener-
ated by coastal sea ice divergence in late winter and
subsequently enhanced by thermodynamical feedbacks
in spring and summer. These findings agree with many
studies (Rigor et al. 2002; Rigor and Wallace 2004;
Strong et al. 2009; Ogi et al. 2010; Wu and Zhang 2010;
Stroeve et al. 2011; Wettstein and Deser 2014; Williams
et al. 2016; Itkin and Krumpen 2017) that have demon-
strated the link between the AO/NAO, sea ice di-
vergence, and Arctic sea ice concentration anomalies.
Here, we extend this link back to the stratosphere.
We have also shown, albeit with rather limited sam-
ples, that similar SIC patterns following SSW and SPV
events exist in the observational record, particularly in
FIG. 9. Compositemean SIE anomaly time series as a function of lag (days) for (left) SSWs and (right) SPVs using
ERA-Interim andNSIDC bootstrap sea ice concentration for 1979–2015. (a),(b) Barents Sea (708–808N, 208–608E);and (c),(d) Laptev, East Siberian, and Chukchi Seas (658–808N, 928–2008E). Composites consist of 8 SSWs and 14
SPVs.Days that are statistically significant at the 90% level using a Student’s t test are indicated by the solid red line.
Gray shading shows 61 standard deviation across the SSW and SPV events. The black vertical line at day zero
indicates the central date of the SSW and SPV events.
7180 JOURNAL OF CL IMATE VOLUME 31
the BA region, where the model and the observations
are in good agreement. This gives us confidence that our
analysis derived from the model has identified a poten-
tially important stratospheric source of Arctic sea ice
predictability.
One potential source of uncertainty associated with
this work is the WACCM climatological sea ice thick-
ness bias, particularly along the Eurasian coast, and
whether this bias has an influence on our results. Our
preliminary assessment is that this bias likely makes the
magnitude of the SIC anomalies that we identify con-
servative. The wind-driven atmosphere–sea ice coupling
is likely weaker in WACCM given that the climatolog-
ically thicker sea ice would be less responsive to anom-
alies in wind forcing. In addition, when there is
anomalous coastal sea ice divergence in WACCM, the
ice–albedo feedback associated with ice drift away from
the coastline would likely be diminished as a result of
the presence of climatologically thicker ice. Further
research is required to quantify the effect of the clima-
tological sea ice thickness bias on the strength of
atmosphere–sea ice coupling in WACCM.
Although we did not find anomalies in pan-Arctic
September Arctic sea ice extent following strato-
spheric circulation extremes in either the model or the
observations, we did find significant anomalies in sea
ice extent following SSWs and SPVs in the L/ES/C
region extending into the fall. Our analysis agrees with
the observational study of Williams et al. (2016) that
highlighted the significant relationship between the
winter AO and sea ice divergence in this region and
September Arctic SIE, suggesting that the presence or
absence of stratospheric circulation extremes in win-
ter may play a nontrivial role in determining total
September Arctic SIE when combined with other
factors.
Finally, we note that since stratospheric anomalies
precede AO anomalies in the troposphere, there is
some potential to extend the period over which skillful
prediction of Arctic SIE may be achieved if knowledge
of stratospheric conditions is known. However, trans-
lating stratospheric information into a skillful predictor
of SIE presents challenges. The magnitude of strato-
spheric circulation anomalies is not linearly related to
the subsequent, coupled tropospheric anomalies—
hence the ubiquitous use of composite analysis in the
SSW literature (e.g., Scaife et al. 2016). This charac-
teristic likely makes stratospheric circulation anoma-
lies a poor predictor of Arctic SIE in a linear statistical
modeling framework. Including stratospheric infor-
mation as a binary predictor (i.e., the presence or ab-
sence of SSWs and SPVs) may be a more fruitful
approach, an avenue of research that we plan to pursue.
Acknowledgments. KLS, LMP, and LBT are sup-
ported by the National Science Foundation Office of
Polar Programs, Arctic Research Opportunities, PLR-
1603350. LBT is also supported by the Office of Naval
Research, N000141110977. Model output is archived
on the High Performance Storage System (HPSS)
maintained by the National Center for Atmospheric
Research’s (NCAR) Computational and Information
Systems Lab (CISL). All model output is available upon
request.
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